LBYCPA1_EQ2 Term Project Submission
By: HERNANDEZ, LIM, and PASCUAL
This paper is a report on the trends of confirmed, recovered, death and active cases of three cities namely, Pasig, Quezon City District 4, and Malabon for April 2021. With the analysis of the graphs, the proponents found out that the three cities follow similar trends. That is, the higher the population of the city, the more cases there seem be. However, based on April 2021 statistics, Quezon City District 4 is an outlier in terms of the number of deaths. From observing the trends, the group recommends that the Government closely monitor and apply necessary strict measures in highly populated areas.
COVID-19 is a pandemic that has affected every country in the world, and the Philippines is no exception. Just like every other country, the virus wasn't in the region, but of course, it started spreading at a certain point. It all started back in January 25 when a 39-year old woman was admitted to San Lazaro hospital in Manila who had the symptoms of cough and sore throat. She was then diagnosed to have the coronavirus by the PCR tests. Afterwards, the second case of COVID-19 in the Philippines arose on January 31 wherein he died the following day, February 1 which made it the first confirmed COVID-19 death outside of China.
Overtime, the virus spread and the number of cases went up until President Rodrigo Duterte established a community quarantine of Metro Manila that started from March 15 until April 14. This meant that travel through land, air, and sea from the region was suspended but with exceptions. Due to months in quarantine, the government decided to be more lenient on the restrictions and established a lighter quarantine for the country, which was general community quarantine. This however, most notably at the start of August, caused a spike in the amount of new cases every day and the 7-day average.
Previous data during the quarantine showed new cases everyday at around 200 cases to 300 cases with a 7 day average within the 200 to 300 range. This went up though at the start of June from 400 to 1000 cases every day with a 7 day average within this range as well. Around August is when the spike occurred wherein there were 3000 to 6000 new cases everyday with a 7-day average from 3000 to 4000 which is considerably higher than previous data. Though this was last year in August, the spike can be seen again at around the start of April due to the more lenient and lighter restrictions enforced by the government. The amount of new cases around April 2021 ranged from 8000 to 12000 new cases everyday with a 7-day average around 8000 to 10000 cases. This then prompted the government to enforce enhanced community quarantine again, which did show acceptable results with the new cases and 7-day average decreasing.
Though these are the data for the entire country of the Philippines, our main focus are three major cities within the national capital region, namely Malabon, Pasig, and Quezon, specifically district 4 due to the size of the city.
Pasig City is a first class highly urban city with a population of around 755,000 people. It is located along the eastern border of Metro Manila with Rizal province. The city encountered its first COVID-19 case on March 9 together with Quezon City and Marikina. The response of the Pasig government was that Mayor Sotto declared a state of calamity within the city after six confirmed cases and one fatality was recorded in the city. He also approved anti-panic buying and hoarding of essential goods ordinance within the city.
Malabon City is a first class highly urban city and has a population of around 365 thousand people. It is located north of the city of Manila and it is a primary residential and industrial area. Malabon encountered its first COVID-19 case on March 23. The response of the Malabon government was to provide Php 520000 to each barangay for provision of food during the quarantine. Mayor Oreta also ordered Maynilad to divert unused water supply from closed malls and establishments to residential areas.
Quezon City is a highly urban city and it is the most populous city in the Philippines. It is also the largest city in terms of population and land area within Metro Manila and has a population of around 2.9 million people. It is located southwest of Manila with Caloocan and Valenzuela to the west and northwest. Quezon city encountered its first COVID-19 case on March 9. The response of the Quezon city government was that Mayor Joy Belmonte announced a state of calamity following six confirmed local COVID-19 cases. She enforced 11 barangays to be under extreme enhanced community quarantine restrictions preventing its residents from exiting their own barangays. The government also imposed a liquor ban on March 27 and funded the efforts of the Philippine Red Cross for mobile testing centers in the city for mass testing.
The data for Pasig and Malabon were taken from their respective Public Information Office Facebook Pages, while the data for Quezon District 4 was taken from Quezon City's official website. They may be accessed through the following links. 1) Pasig (https://www.facebook.com/PasigPIO/)
2) Malabon (https://www.facebook.com/OfficialMalabon/)
3) Quezon District 4 (https://quezoncity.gov.ph/covid19counts/)
The data, as taken from the source, was divided into four groups: confirmed, recovered, deaths, and active.
The graphs for Confirmed Cases, Recovered Cases, and Deaths are cumulative while the graphs for the Active Cases are not. This characteristic of the data is the reason behind why the graphs for the active cases constantly exhibit wild fluctuations, whereas the rest only express upward trends.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
pasig_confirmed = pd.read_excel('covid.xlsx', sheet_name = 'PConfirmed')
pasig_recovered = pd.read_excel('covid.xlsx', sheet_name = 'PRecovered')
pasig_death = pd.read_excel('covid.xlsx', sheet_name = 'PDeath')
pasig_active = pd.read_excel('covid.xlsx', sheet_name = 'PActive')
quezon4_confirmed = pd.read_excel('covid.xlsx', sheet_name = 'QConfirmed')
quezon4_recovered = pd.read_excel('covid.xlsx', sheet_name = 'QRecovered')
quezon4_death = pd.read_excel('covid.xlsx', sheet_name = 'QDeath')
quezon4_active = pd.read_excel('covid.xlsx', sheet_name = 'QActive')
malabon_confirmed = pd.read_excel('covid.xlsx', sheet_name = 'MConfirmed')
malabon_recovered = pd.read_excel('covid.xlsx', sheet_name = 'MRecovered')
malabon_death = pd.read_excel('covid.xlsx', sheet_name = 'MDeath')
malabon_active = pd.read_excel('covid.xlsx', sheet_name = 'MActive')
pasig_barangay = list(pasig_confirmed.columns)
pasig_barangay = pasig_barangay[1:-1]
quezon4_barangay = list(quezon4_confirmed.columns)
quezon4_barangay = quezon4_barangay[1:-1]
malabon_barangay = list(malabon_confirmed.columns)
malabon_barangay = malabon_barangay[1:-1]
PasigC = px.line(title = 'Confirmed COVID-19 Cases in Pasig City')
for x in pasig_barangay:
PasigC.add_scatter(x = pasig_confirmed['Barangay'], y = pasig_confirmed[x], mode = 'lines+markers', name = str(x))
PasigC.update_layout(
xaxis={'title':'Day'},
yaxis={'title':'Cases'},
)
PasigC.show()
In many barangays, the cases were mostly contained. It can be observed from the graph that there is a sharper incline for the number of confirmed cases in barangays that started the month with more cases.
PasigR = px.line(title = 'Recovered COVID-19 Cases in Pasig City')
for x in pasig_barangay:
PasigR.add_scatter(x = pasig_recovered['Barangay'], y = pasig_recovered[x], mode = 'lines+markers', name = str(x))
PasigR.update_layout(
xaxis={'title':'Day'},
yaxis={'title':'Cases'},
)
PasigR.show()
The number of recovered cases exhibited increasingly steeper inclines as the month progressed. It can be observed from the graph that there is a sharper incline for the number of confirmed cases in barangays that started the month with more cases.
PasigD = px.line(title = 'COVID-19 Deaths in Pasig City')
for x in pasig_barangay:
PasigD.add_scatter(x = pasig_death['Barangay'], y = pasig_death[x], mode = 'lines+markers', name = str(x))
PasigD.update_layout(
xaxis={'title':'Day'},
yaxis={'title':'Cases'},
)
PasigD.show()
Many of the barangays reported very few deaths throughout the month. However, while the rest of the barangays began the month with less than 40 deaths and concluded the month with less than 50 deaths, the only outlier, Pinagbuhatan, reported 21 deaths for the entire month.
PasigA = px.line(title = 'Active COVID-19 Cases in Pasig City')
for x in pasig_barangay:
PasigA.add_scatter(x = pasig_active['Barangay'], y = pasig_active[x], mode = 'lines+markers', name = str(x))
PasigA.update_layout(
xaxis={'title':'Day'},
yaxis={'title':'Cases'},
)
PasigA.show()
There was a continuous increase in the number of cases until April 13. Then, there was a steep decline for all barangays, except Pinagbuhatan, on April 14. After which, the cases remained relatively stable save for a few small spikes and dips until April 29 when there was another sharp drop in cases.
QuezonC = px.line(title = 'Confirmed COVID-19 Cases in Quezon City District 4')
for x in quezon4_barangay:
QuezonC.add_scatter(x = quezon4_confirmed['Barangay'], y = quezon4_confirmed[x], mode = 'lines+markers', name = str(x))
QuezonC.update_layout(
xaxis={'title':'Day'},
yaxis={'title':'Cases'},
)
QuezonC.show()
The graph shows a continuous upward incline through the month. The incline tends to be steeper for barangays that started the month with more cases.
QuezonR = px.line(title = 'Recovered COVID-19 Cases in Quezon City District 4')
for x in quezon4_barangay:
QuezonR.add_scatter(x = quezon4_recovered['Barangay'], y = quezon4_recovered[x], mode = 'lines+markers', name = str(x))
QuezonR.update_layout(
xaxis={'title':'Day'},
yaxis={'title':'Cases'},
)
QuezonR.show()
It can be observed that for the first 5 days of the month, there was little to no change. By April 6, the graph begins to exhibit a steady upward incline throughout the month for most barangays. The incline tends to be steeper for barangays that started the month with more cases.
QuezonD = px.line(title = 'COVID-19 Deaths in Quezon City District 4')
for x in quezon4_barangay:
QuezonD.add_scatter(x = quezon4_death['Barangay'], y = quezon4_death[x], mode = 'lines+markers', name = str(x))
QuezonD.update_layout(
xaxis={'title':'Day'},
yaxis={'title':'Cases'},
)
QuezonD.show()
The graph exhibits hardly any change in the number of deaths. Most of the barangays reported no deaths for the month. A few had one, but the highest number reported was by San Martin de Porres with only 2 deaths for the entire month.
QuezonA = px.line(title = 'Active COVID-19 Cases in Quezon City District 4')
for x in quezon4_barangay:
QuezonA.add_scatter(x = quezon4_active['Barangay'], y = quezon4_active[x], mode = 'lines+markers', name = str(x))
QuezonA.update_layout(
xaxis={'title':'Day'},
yaxis={'title':'Cases'},
)
QuezonA.show()
There were many fluctuations in active cases throughout the month. From the beginning of the month to April 6, there was a slow rise and a sudden drop in the number of cases. Then, the cases slowly grew again until another drop on April 10. After which, it remained relatively stable until April 15 with another significant drop in cases. By April 23, the cases rose again and just as quickly fell by April 30.
Overall, compared to the beginning of the month, there were far less cases by the end of the month.
MalabonC = px.line(title = 'Confirmed COVID-19 Cases in Malabon City')
for x in malabon_barangay:
MalabonC.add_scatter(x = malabon_confirmed['Barangay'], y = malabon_confirmed[x], mode = 'lines+markers', name = str(x))
MalabonC.update_layout(
xaxis={'title':'Day'},
yaxis={'title':'Cases'},
)
MalabonC.show()
The graph shows a continuous upward incline through the month. The incline tends to be steeper for barangays that started the month with more cases.
MalabonR = px.line(title = 'Recovered COVID-19 Cases in Malabon City')
for x in malabon_barangay:
MalabonR.add_scatter(x = malabon_recovered['Barangay'], y = malabon_recovered[x], mode = 'lines+markers', name = str(x))
MalabonR.update_layout(
xaxis={'title':'Day'},
yaxis={'title':'Cases'},
)
MalabonR.show()
The graph shows a continuous upward incline through the month. The incline tends to be steeper for barangays that started the month with more cases.
MalabonD = px.line(title = 'COVID-19 Deaths in Malabon City')
for x in malabon_barangay:
MalabonD.add_scatter(x = malabon_death['Barangay'], y = malabon_death[x], mode = 'lines+markers', name = str(x))
MalabonD.update_layout(
xaxis={'title':'Day'},
yaxis={'title':'Cases'},
)
MalabonD.show()
The graph exhibits hardly any change. All the barangays, except Longos with 51 deaths by the end of the month, had only 30 or less deaths in total, as of April 30.
MalabonA = px.line(title = 'Active COVID-19 Cases in Malabon City')
for x in malabon_barangay:
MalabonA.add_scatter(x = malabon_active['Barangay'], y = malabon_active[x], mode = 'lines+markers', name = str(x))
MalabonA.update_layout(
xaxis={'title':'Date'},
yaxis={'title':'Cases'},
)
MalabonA.show()
There were many fluctuations in the active cases throughout the month. However, the general trend is a rise in cases, beginning on April 1 and peaking on April 4, until a significant drop on April 10. From there, aside from another spike and drop on April 17, the number of active cases slowly declined.
In all clusters, it can be observed that barangays with more confirmed cases naturally had more recovered cases, active cases, and deaths.
Generally, the confirmed and recovered graphs followed similar slow and steady upward trends. The deaths also stayed relatively level for the month of April despite the number of confirmed cases for the month. The graphs for the active cases were the only ones to show any sort of variation between the 4 groups (confirmed, recovered, death, and active).
ConfirmedAll = px.line(title = "Total Confrimed Cases")
ConfirmedAll.add_scatter(x = pasig_confirmed['Barangay'], y = pasig_confirmed['Total'], mode = 'lines+markers', name = 'Pasig')
ConfirmedAll.add_scatter(x = malabon_confirmed['Barangay'], y = malabon_confirmed['Total'], mode = 'lines+markers', name = 'Malabon')
ConfirmedAll.add_scatter(x = quezon4_confirmed['Barangay'], y = quezon4_confirmed['Total'], mode = 'lines+markers', name = 'Quezon District 4')
ConfirmedAll.update_layout(
xaxis={'title':'Date'},
yaxis={'title':'Cases'},
)
ConfirmedAll.show()
The graph exhibits a slow but continuous upward trend. Pasig and Quezon District 4 reported about 7,300 additional confirmed cases while Malabon had reported a much lower figure at about only 2,500 additional cases.
RecoveredAll = px.line(title = "Total Recovered Cases")
RecoveredAll.add_scatter(x = pasig_recovered['Barangay'], y = pasig_recovered['Total'], mode = 'lines+markers', name = 'Pasig')
RecoveredAll.add_scatter(x = malabon_recovered['Barangay'], y = malabon_recovered['Total'], mode = 'lines+markers', name = 'Malabon')
RecoveredAll.add_scatter(x = quezon4_recovered['Barangay'], y = quezon4_recovered['Total'], mode = 'lines+markers', name = 'Quezon District 4')
RecoveredAll.update_layout(
xaxis={'title':'Date'},
yaxis={'title':'Cases'},
)
RecoveredAll.show()
This graph shows that Malabon had a rather linear slope throughout the month. Meanwhile, Pasig experienced the same until April 14, when a significant number of recovered cases were reported. After which, the reports returned to taking a slow and steady upward trend; albeit, with a slightly steeper slope than before the April 14 spike. On the other hand, Quezon District 4 had few recovered cases until April 6 when it began to follow a trend similar to that of post-April 14 Pasig.
DeathAll = px.line(title = "Total Deaths")
DeathAll.add_scatter(x = pasig_death['Barangay'], y = pasig_death['Total'], mode = 'lines+markers', name = 'Pasig')
DeathAll.add_scatter(x = malabon_death['Barangay'], y = malabon_death['Total'], mode = 'lines+markers', name = 'Malabon')
DeathAll.add_scatter(x = quezon4_death['Barangay'], y = quezon4_death['Total'], mode = 'lines+markers', name = 'Quezon District 4')
DeathAll.update_layout(
xaxis={'title':'Date'},
yaxis={'title':'Cases'},
)
DeathAll.show()
This graph is relatively stable. Pasig reported 147 deaths, Malabon reported 69 deaths, and Quezon District 4 reported 10 deaths.
ActiveAll = px.line(title = "Total Active Cases")
ActiveAll.add_scatter(x = pasig_active['Barangay'], y = pasig_active['Total'], mode = 'lines+markers', name = 'Pasig')
ActiveAll.add_scatter(x = malabon_active['Barangay'], y = malabon_active['Total'], mode = 'lines+markers', name = 'Malabon')
ActiveAll.add_scatter(x = quezon4_active['Barangay'], y = quezon4_active['Total'], mode = 'lines+markers', name = 'Quezon District 4')
ActiveAll.update_layout(
xaxis={'title':'Date'},
yaxis={'title':'Cases'},
)
ActiveAll.show()
This graph exhibits quite a few fluctuations throughout the month for Pasig and Quezon. Both clusters experienced a slow rise until the middle of the month, around April 13-15, when the cases dropped significantly. The cases proceeded to slowly rise again only to slightly drop on April 24. From there, the numbers remained relatively stable until another notable drop on April 29. Meanwhile, the cases in Malabon were showing a slow and steady downward trend, with a few upward bumps on the 4th, 9th, 17th, and 28th.
Comparing the number of cases from the beginning of the month to the end of the month, Pasig had 943 less cases, Quezon District 4 had 825 less cases, and Malabon had 454 less cases.
In general, Pasig, Quezon City District 4 and Malabon’s confirmed cases are inclined and growing, with Pasig taking the lead. The same could be said to their graphs for the recovered cases. Interestingly enough, Quezon City District 4 had very few death cases, causing its graph to be flat instead of inclined like the other two cities.
One notable trend in all the graphs are the high cases of Brgy. Pinagbuhatan for Pasig, Brgy. Bagong Lipunan ng Crame, and, Brgy. Longos. It had 2784, 1322, and 1657 cases by the end of April, respectively. A huge gap compared to the lowest of the Barangays, that is, Brgy. Sta Rosa (27) for Pasig, Brgy. Horseshoe (132) for Quezon City District 4 and Brgy. Bayan-Bayanan (196) for Malabon City. At closer glance, the high cases could be explained by the population of the two extremes between each city. According to a Population Census published in 2015, Pinagbuhatan has the highest population amongst all of the Barangays in Pasig. Pinagbuhatan’s population makes up 20% of the overall population of Pasig. Meanwhile the lowest population size amongst all of the Barangays is to no surprise, Sta. Rosa with a 0.2% contribution to the overall population. Meanwhile in Quezon City District 4, surprisingly, the highest cases Brgy. Bagong Lipunan ng Crame, is not the most populous Barangay in the 2015 census. As for Malabon city, it also follows the trend seen in Pasig, with Longos making up 15% of the overall population of Malabon.
Another feature that stands out among the graphs in the relatively stagnant flow for the deaths in all 3 clusters. This implies that while many are getting infected with the virus, most of them manage to recover.
Based on all the given data and analysis, the group has derived the following insights on what can be done moving forward.
1) For the barangays with considerably higher population and in turn higher cases, such as Brgy. Pinagbuhatan, Brgy. Bagong Lipunan ng Crame, and Brgy. Longos, a more strictly enforced COVID-19 guidelines such as those under enhanced community quarantine. This entails that no gatherings are allowed during the period, a curfew of 8 PM to 5 AM, and only authorized personnel are allowed outside of the households. The suggestion of ECQ-like guidelines even if stated by the President is GCQ, is to give more movement restrictions to the citizens of each barangay, which in turn reduces the spread of the virus to the people within that region.
2) For the other barangays with lower population however, the group recommends the enforcement of GCQ COVID-19 guidelines within the barangay. Though it is more lenient than the aforementioned ECQ guidelines for the bigger barangays, the guidelines of the wearing of mask and face shield, and social distancing must be strictly enforced wherein it is a violation if the guidelines are not followed in any way. This strict enforcement of the guidelines should apply for both, but should be more enforced in the smaller areas assuming they are under GCQ while the larger areas are under ECQ.
3) For all of the barangays in general, a concept implemented by the Cotabato LGU, is the “No Movement Sunday” policy which states that all establishments except for pharmacies and medical buildings are to be closed during the Sunday of every week. This also imposes that nobody is allowed to exit their houses as there are law enforcement personnel roaming the streets which will apprehend citizens if they are seen outside, no matter the excuse. This means that the citizens of the barangay should stock up on their goods the day before the policy is enforced. The advantage of this policy is that the Cotabato city Mayor Cynthia Guiani-Sayadi stated that “It was in October last year when we had recorded the highest number of Covid-19 positive cases and since then, the number slowly declined until we reached the lowest in February,”. The policy restricts movement but not by a majority, lessens the spread of the virus, and gives enough time for health personnel to cure citizens who are COVID-19 positive. In lieu of vaccinations nowadays, the policy can help in organizing and limiting the flow of people in vaccination sites to ensure that there is a lesser transmission of the virus within the area.
1) About Malabon. (n.d.). Malabon City Government. Retrieved June 2, 2021, from https://malabon.gov.ph/about-malabon/
2) About QC. (2021, May 3). Quezon City Government. https://quezoncity.gov.ph/about-the-city-government/
3) First COVID-19 infections in the Philippines: a case report. (2020). PubMed Central (PMC). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7154063/
4) Moya, J. (2021, April 29). MECQ and ECQ: What's The Difference And Guidelines For 12 to 30 April. Tatler Philippines. https://ph.asiatatler.com/life/the-difference-between-ecq-and-mecq
5) Pasig City. (n.d.). Pasig City Government. Retrieved June 2, 2021, from https://www.pasigcity.gov.ph/facts-and-figures
6) Pasig City (Philippines): Barangays - Population Statistics, Charts and Map. (n.d.). City Population. Retrieved June 2, 2021, from https://www.citypopulation.de/en/philippines/pasig/
7) Wikipedia contributors. (2021, June 1). COVID-19 pandemic in Metro Manila. Wikipedia. https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Metro_Manila
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